AI-Based Power Quality Enhancement in Grid-Connected Solar PV Systems
Keywords:
DSTATCOM, Power Quality, Voltage Sag, Voltage Swell, Harmonics.Abstract
The increasing integration of solar photovoltaic (PV) systems into modern power grids has introduced significant challenges in maintaining power quality due to intermittency, nonlinear loads, and grid disturbances. This research presents an advanced Artificial Intelligence (AI)-based approach for power quality enhancement in grid-connected solar PV systems using Artificial Neural Networks (ANN). The proposed methodology focuses on mitigating key power quality issues such as voltage sag, swell, harmonic distortion, and reactive power imbalance. An ANN-based control strategy is developed to optimize the performance of power electronic compensators, such as shunt active power filters (SAPF) under varying environmental and load conditions. The neural network is trained using real-time and simulated datasets to accurately predict system disturbances and generate appropriate compensating signals. The model demonstrates adaptive learning capability, enabling improved dynamic response and robustness compared to conventional controllers such as PI and fuzzy logic systems. Simulation results, carried out in MATLAB/Simulink environment, validate the effectiveness of the proposed ANN-based controller in reducing Total Harmonic Distortion (THD) within IEEE standard limits and maintaining voltage stability at the point of common coupling (PCC). The system also shows enhanced efficiency in reactive power compensation and improved grid synchronization under fluctuating solar irradiance conditions. The findings of this study highlight the potential of ANN-based intelligent control systems in ensuring reliable and high-quality power delivery in renewable energy-integrated grids. This work contributes toward the development of smart and sustainable energy systems by providing a scalable and efficient solution for power quality management in solar PV applications.
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